COS 121-10
Forecasting climate change impacts on plant population dynamics at large spatial extents: A test case with sagebrush (Artemisia) species

Thursday, August 14, 2014: 4:40 PM
314, Sacramento Convention Center
Andrew Tredennick, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO
Peter B. Adler, Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT
Andrew O. Finley, Forestry and Geography, Michigan State University, East Lansing, MI
Cameron L. Aldridge, Fort Collins Science Center, U.S. Geological Survey, Fort Collins, CO
Collin G. Homer, United States Geological Survey Earth Resources Observation and Science, Sioux Falls, SD
David T. Iles, Biology, Tufts University, Medford, MA
Andrew R. Kleinhesselink, Department of Wildland Resources, Utah State University, Logan, UT
Eric M. LaMalfa, Wildland Resources, Utah State University, Logan, UT
Rebecca Mann, Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT

Forecasting the impacts of climate change on plant communities over large spatial extents requires understanding population dynamics at similar extents. However, virtually all studies of plant population dynamics rely on demographic observations at the meter to sub-meter scale, and extrapolating small-scale studies to large spatial extents is difficult because the data likely only represent a small subset of environmental conditions. Large-scale trends in populations are easily detected using available monitoring data, but such data are rarely used to project future population states. We propose combining the best features of local-scale population modeling and large-scale monitoring data by modeling population dynamics at large spatial extents based on the theory and mechanics of individual-based models. We illustrate this approach with a 27 year remotely-sensed time series of sagebrush (Artemisia spp.) cover in southwestern Wyoming (5 x 5 kilometer study area). Our pixel-based model (PBM) consists of vital rate regressions in which sagebrush growth (change in cover), survival (probability of continued occupancy), and colonization (probability of transition from unoccupied to occupied) of 30 m pixels is a function of climate covariates. We then use the fitted PBM to forecast sagebrush cover at the landscape scale in response to projected temperature and precipitation changes.


The PBM successfully reproduces the observed sagebrush cover distribution, with the simulated mean (13.7%) and standard deviation (4.5) similar to the observed time-series distribution (mean = 14%; s.d. = 4.4). Based on averaged outputs from climate models for the 2050-2100 period, relative to the 1950-2000 period our study area is projected to experience increased temperature and precipitation. Under projected future climate, we forecast a positive shift in mean sagebrush cover from 14% to between 16-17% depending on the emissions scenario. Many other studies suggest climate change will negatively impact sagebrush populations. Our study site is at high elevation, and so the positive response of sagebrush growth to temperature may be isolated to similar high elevation sites existing near the lower thermal limit for sagebrush species. The benefit of our pixel-based modeling approach is that climatic responses in other areas can easily be analyzed. As access to species-specific time-series from remote sensing increases, our method will provide a way to forecast the trajectories of plant populations at scales relevant to land management decisions.